System and method for nanoscale X-ray imaging of biological specimen

ABSTRACT

System and method for nanoscale X-ray imaging of biological specimen. The imaging system comprises an X-ray source including a plurality of spatially and temporally addressable electron sources, an X-ray detector arranged such that incident X-rays are oriented normal to an incident surface of the X-ray detector and a stage arranged between the X-ray source and the X-ray detector, the stage configured to have mounted thereon a biological specimen through which X-rays generated by the X-ray source pass during operation of the imaging system. The imaging system further comprises at least one controller configured to move the stage during operation of the imaging system and selectively activate a subset of the electron sources during movement of the stage to acquire a set of intensity data by the X-ray detector as the stage moves along a three-dimensional trajectory.

RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patent application Ser. No. 16/325,936, filed Feb. 15, 2019, titled “NANOSCALE X-RAY TOMOSYNTHESIS FOR RAPID ANALYSIS OF INTEGRATED CIRCUIT (IC) DIES”, which is a national stage filing under 35 U.S.C. § 371 of International Patent Application Serial No. PCT/US2017/047030, filed Aug. 15, 2017, titled “NANOSCALE X-RAY TOMOSYNTHESIS FOR RAPID ANALYSIS OF INTEGRATED CIRCUIT (IC) DIES”, which claims the benefit of U.S. Provisional Patent Application No. 62/375,503, filed Aug. 16, 2016, titled “NANOSCALE XRAY TOMOSYNTHESIS FOR RAPID ANALYSIS OF IC DIES (NEXT-RAID),” each of which is hereby incorporated by reference in its entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with Government support under Grant No. FA8650-17-C-9113 awarded by the Air Force Research Laboratory (AFRL). The Government has certain rights in the invention.

BACKGROUND

Microscopic X-ray imaging techniques can be broadly classified into two categories: those that are direct-image forming (i.e., the image formed on the sensor directly corresponds with the imaged object), and those that rely on computational imaging to reconstruct an image of the object. Direct-imaging utilizes a combination of X-ray optical elements including: Fresnel zone plates, Laue lenses and crystal anlyzers, and highly-polished mirrors in the image formation process. Although these techniques have achieved sub-10 nm resolution, the considerable energy loss introduced by X-ray optical elements has meant that these techniques all require a substantial photon flux, which has so far only been available in larger facilities (e.g., at synchrotrons). As a result, direct-image forming X-ray microscopy is largely unsuitable for compact imaging setups.

Computational X-ray imaging involves acquiring a sequence of images under different imaging geometries or conditions (an approach also referred to as “diversity”) and computationally combining these images (which individually may not look like the object at all) together to reconstruct a representation of the object. This way, it is possible, for example, to reconstruct phase contrast information in addition to standard attenuation information, the former often holding more information about the underlying object. The computational approaches to X-ray phase imaging can be classified into three main categories: (1) methods based on the use of crystal analyzers, (2) propagation-based imaging methods, and (3) grating-based interferometry.

SUMMARY

Some embodiments are directed to an imaging system for imaging a biological specimen. The imaging system comprises an X-ray source including a plurality of spatially and temporally addressable electron sources, an anode that generates X-rays from the electrons, an X-ray detector arranged such that incident X-rays are oriented normal to an incident surface of the X-ray detector, a stage arranged between the X-ray source and the X-ray detector, the stage configured to have mounted thereon the biological specimen through which X-rays generated by the X-ray source pass during operation of the imaging system, and at least one controller. The at least one controller is configured to move the stage during operation of the imaging system and selectively activate a subset of the electron sources during movement of the three-axis stage to acquire a set of intensity data by the X-ray detector as the three-axis stage moves along a three-dimensional trajectory.

Other embodiments are directed to a method of imaging a biological specimen using an imaging device comprising an X-ray source having a plurality of electron sources, an X-ray detector and a stage arranged between the plurality of electron sources and the X-ray detector and configured to have mounted thereon the biological specimen. The method comprises moving, using at least one controller, the stage during operation of the imaging system and selectively activating a subset of the electron sources during movement of the three-axis stage to acquire a set of intensity data by the X-ray detector as the three-axis stage moves along a three-dimensional trajectory.

Some embodiments are directed to a long lifetime modular cathode based on a 2D array of field emitters of electrons, each individually regulated by a silicon nano-wire current limiter.

Some embodiments are directed to an array of nano-focused electrons for nano-sharp X-ray sources using a micro-channel cooled transmission-type anode for generating characteristic X-rays with low background of bremsstrahlung emission.

Some embodiments are directed to an X-ray imaging platform that combines absorption and phase contrast with a potential to distinguish between several materials with low atomic number (Z).

Some embodiments are directed to algorithms for coded source imaging with an ability to boost end-to-end image resolution by over 1000×.

Some embodiments are directed to phase-contrast and absorption-contrast radiography and tomosynthesis of biological specimen such as cellular and sub-cellular structures.

Some embodiments are directed to labeling of the biological specimen with a labeling agent.

The foregoing summary is provided by way of illustration and is not intended to be limiting.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

FIGS. 1A and 1B illustrate components of an imaging system for imaging an integrated circuit (IC) in accordance with some embodiments;

FIG. 2 illustrates a field emitter array X-ray source for use with some embodiments;

FIG. 3 illustrates a cold cathode structure for use with some embodiments;

FIG. 4A illustrates a field emitter array with an electron transparent gate for use with some embodiments;

FIG. 4B is a plot of I-V characteristics of the device as shown in FIG. 4A;

FIG. 5A illustrates an anode structure for use with some embodiments;

FIG. 5B is a plot of an example of characteristic X-ray spectra as a function of acceleration voltage for the anode structure of FIG. 5A; and

FIG. 6A schematically shows operating principles of an imaging device designed in accordance with some embodiments;

FIG. 6B shows a plan view diagram of an exemplary imaging system 600, in accordance with some embodiments;

FIG. 7 is a schematic diagram illustrating two exemplary approaches to individually address field emitters, in accordance with some embodiments;

FIG. 8A is a schematic diagram illustrating a p-n junction connected in series with a field emitter, in accordance with some embodiments;

FIG. 8B is a schematic energy band diagram of an exemplary device under operation;

FIG. 9 is a schematic flow diagram showing an exemplary process 900 for computational imaging and reconstruction of a biological specimen, in accordance with some embodiments.

DETAILED DESCRIPTION

Rapid advances have been observed in 3D volumetric imaging of integrated circuits using X-ray microscopes in the past few years. Such advances are typically based on Fresnel zone plates, computed tomography and a monochromatic X-ray source. At least two approaches have been demonstrated. One approach is based on a laboratory X-ray source. It has low photon flux because of the need for beams with directional X-rays that are nearly mono-chromatic. Another approach is based on a synchrotron, which has high photon flux. Both approaches demonstrated sub-30 nm resolution; however, the wall plug energy efficiency for generating the “coherent” monochromatic X-ray beam is of the order of 10⁻⁸. This places a severe limitation on either the speed or location of image acquisition. Ideally, an X-ray microscope that has the same form factor and cost as a semiconductor test tool is desired.

In the realm of laboratory sources, grating-based interferometry and free space propagation based imaging are the most commonly used techniques for X-ray phase imaging. In recent years, grating-based interferometry has seen significant advancements in quality and has even seen extensions into tomographic imaging. However, to accommodate ˜10-nanometer scale imaging resolution, gratings become subject to severe manufacturing errors; this imposes a significant resolution loss. With non-synchrotron sources, propagation based imaging is likely to provide the highest possible spatial resolution for X-ray phase imaging. In propagation based imaging, a full solution for attenuation and phase requires multidimensional intensity measurements. Several methods have been proposed to reduce the number of intensity measurements needed for reconstruction, thus significantly reducing acquisition complexity. Of particular interest are methods based on the Contrast Transfer Function (CTF) and the Transport of intensity Equation (TIE). CTF methods assume weak absorption of the object, which does not hold when imaging ICs. TIE methods, on the other hand, do not impose any restrictions other than the paraxial approximation, which is hard to violate in the X-ray regime; moreover, TIE can recover the optical density (or optical path length, OPL) of the object even under spatially partially coherent illumination conditions (under such conditions, “phase” becomes ill-defined, but it is usually the OPL that is of interest).

In accordance with some embodiments, TIE-based techniques are combined with coded source imaging to simultaneously retrieve high-resolution phase contrast as well as 3D information about the imaged object(s). For samples assumed to be thin, multi-angled illumination has traditionally been utilized to provide information about the phase of the imaged object or enhanced resolution. However, images obtained via multi-angle (coded source) illumination contain not only information about the phase of the object (enabling phase contrast imaging and enhanced resolution) but are also partially tomographic in nature, enabling tomosynthesis.

Digital tomosynthesis, which was originally developed as an alternative modality for projection X-ray mammography, has become a general-purpose tomographic technique that is especially suited for objects with planar organization. It is widely used because of its high in-plane resolution, high contrast resolution, and high detectability of target regions. While it is generally not applied for planar reconstruction of IC dies, the inherent strengths of this technique make it especially suitable for imaging ICs. In digital tomosynthesis, the detector plane and object are typically fixed; and the X-ray source rotates or translates to provide only a limited number of angular projections. The digital 3-D image is reconstructed, plane by plane, from these projection measurements. There are two drawbacks to this approach. Firstly, translating/rotating gantries introduce significant additional mechanical instability to the system, resulting in motion blur or misalignment artifacts. Secondly, limited angular views fail to adequately sample Radon space, and traditional reconstruction methods produce severe artifact and non-isotropic depth resolution when applied to these limited projections. To achieve very high spatial resolutions (e.g., on the order of nm), the image quality using conventional filtered back-projection (FBP) techniques is unsatisfactory due to limited angular projections.

To address these issues, instead of using mechanical rotation to perform tomosynthesis, some embodiments simulate rotation through a coded source. By translating the illumination pattern across emitters on the source, measurements can be made in a static, mechanically stable manner. The simulated translation can occur in 2D, enabling a more complete coverage of Radon space and resulting in a more accurate image reconstruction.

High-yield manufacturing of integrated circuits (ICs) at feature sizes of 7-10 nm requires high-speed and high-resolution image acquisition for process verification and failure analysis. This task is further compounded by the fact that with increasing number of interconnect-layers, IC chips have become three-dimensional. The introduction of new technologies such as “through silicon via” (TSV) have made possible vertical stacking of IC chips from different technologies to form “Systems-on-a-Chip” (SOC). Most in- and post-process inspection tools are two-dimensional making it extremely difficult to debug fabrication processes without labor-intensive “layer-by-layer” imaging with subsequent assembly of the images, a process that is very susceptible to errors. It has thus become imperative to develop alternative approaches that enable volumetric imaging of IC chips. A potential approach to non-destructive imaging of IC chips in a time-sensitive manner requires the use of penetrating radiation such as X-rays. Assuming a typical IC chip having characteristics—1 cm×1 cm size, 13 layers of metal, 13 layers of contact metal and silicon layers that include the shallow trench isolation—when scanned at a resolution of 10 nm, the number of voxels in a three dimensional image of the chip would be of the order of 10¹⁵, This creates an enormous challenge for image acquisition and processing.

To this end, some embodiments are directed to an analysis tool (also referred to herein as “NeXT-RAID”) capable of volumetric imaging of Si IC chips with minimum size circuit features down to 10 nm. In some embodiments, NeXT-RAID enables 3D reconstruction of IC chips for circuit debugging or failure analysis and is capable of acquiring images and reconstructing all circuit layers with 100% accuracy in less than 25 days. Other embodiments are directed to conducting an “inverse chip layout” to translate 3D reconstructed images into a mask layout.

Some embodiments are directed to a system for non-destructively acquiring images using an X-ray microscope and reconstructing the images in 3D using tomosynthesis and computational imaging for “virtual delayering” IC chips at an isotropic resolution to 10 nm via an overall image magnification of >1000.

Some aspects of the present application are related to nanoscale X-ray imaging of biological specimen. The inventors have recognized and appreciated that in biological and clinical research, there is a need for analytical tools that enable visualization of cell morphology and function at nanometer scale. X-ray microscopes, especially those in the soft X-ray regime of 10 to 0.05 angstroms, have a much lower Rayleigh resolution limit compared to optical microscopes and provide a mesoscale resolution that is in between light microscopy and electron microscopy. X-rays have an excellent penetration for soft-tissue specimens enabling imaging of specimens in their native state. Moreover, elaborate sample preparation or chemical fixation is not required, and it is also possible to tune X-ray imaging to the water window for improved contrast.

The inventors have recognized and appreciated that the X-ray absorption of most cellular and sub-cellular structures is very similar. Therefore, in a conventional X-ray image utilizing absorption contrast, one cannot visualize low-contrast cellular organelles—primarily made of low atomic number materials such carbon, hydrogen, oxygen and nitrogen—against a background of water and lipids. Aspects of the present application are directed to an imaging system and methods for operating the same for X-ray imaging a biological specimen without the limitations described above.

The imaging systems and methods for using the same to image biological specimens are in many aspects similar to embodiments of the present application for imaging integrated circuits. In some embodiments an imaging system as disclosed herein may be used for imaging both integrated circuits and a biological specimen, as the use of such imaging systems is not so limited to a particular type of samples.

In one aspect, 5 nm diameter X-ray sources in large arrays are provided. In some embodiments, such an X-ray source array may be fabricated with integrated circuit technology, for example by fabricating silicon field emitter arrays with nano-sharp tips. In one example, arrays as many as 108 or more sources can be fabricated.

According to an aspect, some embodiments are compact, tabletop instruments that could be placed in a research lab, pathology suite or an operating room for fast imaging of tissue specimens with minimal preparation (e.g., a thin, microtome slabs of tissue suspended in a matrix, e.g., Matrigel or paraffin, or presented on a thinned glass slide) in under 15 minutes. Widespread availability of such an instrument can transform therapy selection, shed light on mechanisms of drug resistance, and aid in drug discovery by curtailing the combinatorial explosion at an earlier stage in the development cycle.

The imaging systems disclosed herein can provide 3D tomographic views of cellular and sub-cellular structures and their interactions with exogenously administered agents. visualize and 3D render microtome slices, several microns in thickness, in their native hydrated state without staining. It will also provide quantitative information about the absorption (i.e., the linear attenuation coefficient) as well the phase (e.g., the real part of the complex refractive index) of the specimen in a tomographic fashion. Some embodiments can provide a spatial resolution of 10 nm, and will be able to localize protein and macro molecular complexes within a cell. In some embodiments, the imaging, without processing and reconstruction, will take no more than 15 minutes. State-of-the-art GPUs may be used for fast, 3D iterative reconstruction of the specimen.

Further according to some aspects, the imaging systems disclosed herein can directly visualize transfection of a cell by a virus particle. Similarly, gene expression and ensuing protein transcription/localization result in alterations in cell structure that can be captured at a resolution of 10 nm. Furthermore, by conducting phase-contrast imaging at this resolution, X-ray nanoscope will enable visualization of the organization, localization, and alterations in the number of sub-cellular structures.

In some embodiments, imaging a biological specimen using an X-ray Nanoscope of the type as disclosed herein includes labeling the biological specimen with one or more labeling agent that can be used as a molecular imaging marker. The labeling agent may comprise a heavy element with a large atomic number Z, for example a metallo-nanoparticle such as Ferumoxytol. In some embodiments, the labeling can be implemented by modifying antibodies in general with gadolinium or other agents.

FIG. 1A schematically illustrates components of an imaging system 100 in accordance with some embodiments. System 100 includes an array of spatially and temporally addressable nano-focused X-ray sources 110. In some embodiments, X-ray sources 110 are based on cold cathodes, an example of which is discussed in more detail below. System 100 also includes a high precision platform 112 for nano positioning of sample specimen such as IC chips or biological specimen. In some embodiments high precision platform 112 is integrated with a high precision nano-positioning stage that has, for example, 1 nm translation resolution and 7 mm travel in the x-y dimensions, 0.2 nm resolution and 100 μm travel in the z-direction and 360 degrees rotation.

System 100 also includes X-ray optics 114 for image magnification and focusing and a high resolution X-ray detector 116 consisting of an array of sensing elements. Modern imaging detectors for soft X-rays (e.g., cooled CCDs) operate in either direct mode where the X-ray interacts in the silicon pixel, or in indirect mode where a phosphor is deposited on the pixel. In the 10-3D keV energy range, the indirect approach is typically the most practical as it avoids the use of specialized imagers such as deep depletion, back illuminated devices.

In some embodiments, X-ray detector 116 is implemented as a high-resolution camera. The high-resolution camera may be arranged so that the incident X-rays are normal to the plane of the camera chip, minimizing parallax effects in the phosphor. Pixel size, though important for resolution, can to some extent be adjusted through changes in magnification. The large magnification in some embodiments yields a corresponding large number of pixels that must be available for imaging. For example, for an ultimate resolution of 10 nm, a 1 mm (10⁶ nm) sized imaging area on the sample specimen will have 10⁵ pixels on a side (10⁶ nm/10 nm pixel size). Therefore 10 Gpixels may be required for storing the image of a 1 mm×1 mm section of an imaged sample specimen, for example with a total of 1000 Gpixels required for imaging an entire IC assuming a 1 cm×1 cm chip size. Multiple such images may be acquired and processed by a tomosynthesis algorithm in accordance with some embodiments, as discussed in more detail below.

Some embodiments utilize reconstruction and imaging that is localized such that it is not necessary to obtain all of the pixels comprising the chip in order to reconstruct a localized area. Although Gpixel arrays have been implemented by stitching together many sensors, some embodiments limit the size of the scanned area on the chip to closely match large commercial chips. Recent work has shown that commercial DSLR cameras can be made to perform at the level of cooled CCDs with the advantage of lower cost and commercial availability. For example, the imager in a modern DSLR camera such as a Nikon D810 is 7360×4912 pixels (36.3 Mpixels) with 4.88 μm pixels. When coupled with a scintillator plate and magnification, such a camera may be used as the detector 116 in accordance with some embodiments. Optical magnification may be used with the DSLR camera to achieve an effective pixel size of 1 micron on the scintillator.

System 100 also includes one or more processing computers 118 comprising at least one processor programmed to perform a tomosynthesis reconstruction of 3D images and one or more one or more displays 120 coupled to processing computer(s) 118 for displaying results of processing performed by processing computer(s) 118. System 100 also includes power and control unit 122 configured to provide operating power to one or more components of system 100. System 100 is configured to image a sample specimen 124. FIG. 1B shows an alternative view of some components of system 100.

In some embodiments, system 100 is configured to perform one or more of coded source imaging, computational imaging for improving image resolution, and 3D image reconstruction for visualization. Below is a more detailed description of the components in system 100 including non-limiting examples for implementing each component. For ease of description, the components of system 100 are grouped into four major categories: (A) Nano-focused X-ray source, (B) X-ray optics for tomography, (C) High-resolution image acquisition and tomographic reconstruction, and (D) An analytical framework for justification of expected performance metrics.

Spatially and Temporally Addressed Nano-Focused X-Ray Source

X-ray source(s) 110 in accordance with some embodiments includes an array of cold cathodes with electron beams focused down to 5 nm using Einzel lenses. Electrons from the e-beams are accelerated to high voltage (20-40 kV) in a vacuum enclosure and collide with a transmission-type anode made of an array of high-Z metals on a low-Z membrane (e.g., a Be window) to generate characteristic X-rays with bremsstrahlung X-ray background suppressed. The electron sources can be spatially or temporally addressed leading to spatial and temporal modulation of the X-rays.

A high current density and a long lifetime cold cathode capable of operating in poor vacuum is achieved in some embodiments by virtue of: (1) coating the field emitter tips with thin layer of Ir/Pt, (2) individually limiting current through them by fabricating a high aspect ratio silicon nanowire in series with them, and (3) isolating the emitters via a thin membrane window that is transparent to electrons emitted on the ultra-high vacuum (UHV) side but impervious to gas molecules or ions generated on the poor vacuum side. This structure, fabricated using MEMS technology, forms a Modular Cold Cathode for use with some embodiments that keeps the field emitter tips under UHV and a pristine environment while the rest of the X-ray source may be in a relatively poor vacuum.

FIGS. 2A and 2B illustrate exemplary cold cathodes for use with some embodiments. The cold cathodes demonstrated high current density silicon field emitter arrays with potential for long lifetime at low operating voltages. Silicon field emitter arrays (FEAs) that demonstrated current densities >100 A/cm² at gate-emitter voltages <75 V were previously reported. These are the highest current densities ever reported for a semiconductor FEA, and approach the current densities of Spindt-type metal cathodes. The reported results were achieved using a new device structure that employs high-aspect-ratio silicon nanowire current limiters 210 in series with each emitter 212 tip to address the major failure mechanisms in FEAs. These current limiters mitigate emitter tip failure due to joule heating thus allowing for higher reliability. A novel fabrication process was employed to produce small gate apertures (˜350 nm) that are self-aligned to the field emitter tip enabling device operation at >100 A/cm2 with gate-to-emitter voltages that are <75 V. The high current density (J>100 A/cm²) cold cathode based on silicon FEAs operated at low voltage (V_(GE)<60 V), and had long lifetime (T>100 hours @ 100 A/cm², T>100 hours @ 10 A/cm², T>300 hours @ 100 mA/cm²).

FIG. 2A shows a 3-D rendering of a device structure for a cold cathode for use with some embodiments. For clarity, layers have been omitted in different regions of the rendering to show detail. In the front, the bare silicon nanowires (200 nm diameter and 10 μm height) with sharp tips are shown. FIG. 2B shows a top view of a fabricated device with 350 nm gate aperture and 1 μm tip-to tip spacing.

In some embodiments, the current density and lifetime of cold cathodes has a lifetime of 250 hours @ current density of J=100 A/cm² for small sized arrays and 100 hours @ J=100 A/cm² for medium sized arrays. This is accomplished by (a) improving the mesa formation process to remove the sharp ridge formed at the perimeter of the mesa that is believed to lead to Time Dependent Dielectric Breakdown (TDDB) and degrade lifetime of the cathodes, (b) using a process for depositing a thin noble metal (e.g., Ir/Pt) coating on the silicon tips and (c) optimizing the tip etch process to reduce the tip radius dispersion to σ_(rtip) 1.00 nm using a tri-level resist process. Some device structures in accordance with some embodiments address at least four main challenges that have thus far prevented field emission devices from attaining high performance and long lifetime: (1) breakdown of the insulator between the emitter substrate and the extraction gate due to charge injection into the insulator, (2) emitter tip burnout due to Joule heating, thermal runaway, or cathodic arcs, (3) emitter tip erosion due to bombardment by back-streaming ions generated from gas molecules desorbed from the anode, gate or other surfaces in the VED and (4) the large capacitance between the gate and the substrate which leads to large stored energy and hence potential for catastrophic failure and limited high frequency performance.

Some embodiments include an electron transparent window structure, which together with the field emitter array constitutes the Modular Cold Cathode Structure shown in FIG. 3. FIG. 3 illustrates a modular compact cathode based on a 2D array of self-aligned gated field emitters individually regulated by silicon nanowire current limiters and a thin membrane anode that is transparent to emitted electrons on the ultra-high vacuum (UHV) side but impervious to gas molecules on the poor vacuum side. In one implementation, the pressure differential on the membrane may be approximately 10 Torr.

The cathode structure shown in FIG. 3 and fabricated using MEMS technology uses a thin membrane window 310 that is transparent to electrons but impervious to gas molecules and ions. The membrane window 310 may include a single or multiple layers of material, examples of which include, but are not limited to, graphene, silicon nitride and amorphous silicon. The Modular Cold Cathode Structure may be modeled using multi-physics simulation packages that include mechanical, thermal, electromagnetic, electron trajectory, and electron transport through membranes. FIGS. 4A and 4B show an example of a field emitter array with an electron transparent gate that demonstrates the feasibility of an electron transparent window for use with some embodiments. As shown, FIG. 4A illustrates a Field Emitter Array with one Electron Transparent Graphene Gate showing that the graphene layer is transparent to emitter electrons at extraction gate voltages as low as 40 V, as confirmed by the I-V characteristics of the device as shown in FIG. 4B.

FIGS. 5A and 5B shows an example of an anode structure for anode 312 that may be used in accordance with some embodiments. In one implementation, a Beryllium (BE) substrate 510 and X-ray generating metal layers with appropriate core level transitions are used. Anode 312 is designed to generate characteristic X-rays with photon energies from 10-30 KeV. Due to waste heat generated in a small volume, some embodiments include integrated cooling microchannels 512 to dissipate the heat. FIG. 5A shows a transmission anode consisting of a cooling manifold made of Be, and a monometallic anode plate. The thickness of Be in the anode window 510 may be matched to that required for filtering out very low energy radiation. FIG. 5B shows an example of characteristic X-ray Spectra as a function of acceleration voltage. Some embodiments are focused on increasing thermal loads and as well as for obtaining improved X-ray signals.

The high cathode emitter density and tight focus of some embodiments result in large heating rates at the anode, which may thermally limit the operating and design space of the electron gun. Previous anode design work was successful in identifying non-rotating designs capable of dissipating 500 W/cm² over 1 μm spots with a 10% duty cycle. Some embodiments involve much smaller (e.g., 1/200) spots and may allow for higher duty cycles (e.g., up to 100% duty cycles). While some previous microchannel-cooled anode designs had some margin for requirements growth, they were not capable of accommodating these conditions, and thus represent a critical barrier to fully exploit the capabilities of the cathode array. In some embodiments, the thermal management capabilities of the device are scaled up by at least an order of magnitude to avoid down-rating of the gun. This is accomplished in some embodiments through aggressive reduction of microchannel scale and pitch.

The anode designs to date have not been optimized for X-ray generation, in terms of tailoring the material, geometry and design features. Improving the X-ray beam by reducing/eliminating electron backscatter and tailoring the anode to provide optimum X-ray transmission, without compromising the already-stressed thermal performance, offers cleaner imaging and the prospect for reduced imaging times. Faraday cups and anode feature shaping are used in some embodiments to suppress electron backscatter, and the material layers and thicknesses are varied to optimize the X-ray transmission spectra.

Electrons emitted from field emitter tips that have a radius of 5±1 nm should be focused to the same size at the anode. This creates a challenge because of the high current densities (e.g., 10⁶ A/cm²) involved. The electron beams have the potential to blur because of space charge. In some embodiments, a global focusing structure 514 is included to focus each individual beamlet on the anode.

According to an aspect, there are two ways to individually address each field emitter. This will enable several downstream features such as structured illumination and coded source imaging. FIG. 7 is a schematic diagram illustrating two exemplary approaches to individually address field emitters, in accordance with some embodiments. The first approach is to electrically address the individual pillars using an addressing circuit. The second approach is to use an infra-red laser beam to generate electron-hole pairs in the vicinity of the Si nanopillars.

In the electrical addressing scheme, a CMOS chip which has open drain high voltage LD-MOSFETs is used to drive the FEA through the emitter contact. The drains of the LD-MOSFETs are connected in series with the emitters of the FEA. The gate of the LD-MOSFET is controlled by a circuit which is fed by row and column addressing lines and the data that will be written into a storage capacitor (or latch) at the gate of the LD-MOSFET. This requires the bonding of two chips—(a) the double-gated field emitter array chip and (b) CMOS addressing chip that will enable individual addressing.

The second approach to addressing the field emitter arrays is based on a laser beam incident on a semiconductor that will create hole-electron pairs where the photon beam is incident. If the photon beam can be tightly focused, then a very small diameter pipe of electron/hole pair is created. To create a photo-detector, a p-n or p-i-n junction is connected in series with a silicon field emitter, and the gate is biased such that electrons drift to the tip from which they are emitted. An exemplary Si field emitter array optical photocathode device 800 with a p-i-n junction 802 connected in series with a field emitter array 804 is shown in FIGS. 8A and 8B.

As shown in FIG. 8A, a p-n junction 802 is connected in series with a field emitter 804. FIG. 8B is a schematic energy band diagram of the device 800 under operation. In FIG. 8B, a photon beam striking the backside of the device creates hole electron pairs. Under proper bias, the electrons drift to the field emitter tip from where they are emitted into vacuum.

X-ray Imaging Detector

According to an aspect, an imaging system disclosed herein may have an X-ray detector capable of 10 nm resolution at the iso-center of the s ample specimen. Since practical X-ray imagers have resolutions of 5 μm or more, a geometrical magnification of 1000 or more is required to obtain 10 nm resolution. Such a magnification is feasible only if the focal spot size of the X-ray beam is 5 nm or less. It should be appreciated that in this model, the size of the pixels on the detector is not important since we can always move the detector out further, increasing geometric magnification. The area imaged on the specimen under examination is therefore dependent only on the total number of pixels in the imager, which favors systems with large numbers of pixels, thereby increasing the area over which data can be acquired and hence throughput. At a resolution of 10 nm, a 1 Mega pixel imager (1 k×1 k) would image a 10 μm×10 μm section of the specimen.

Some embodiments include an X-ray energy range of 30 kVp and below, where it is possible to have either energy-integrating or photon-counting X-ray detectors. In cooled CCD and CMOS imagers, which generally have smaller pixels, the signal is integrated over a frame time and therefore cannot distinguish individual X-rays. On the other hand, photon-counting detectors count individual X-ray photons and bin them according to their energy. Recent developments in these photon-counting X-ray detectors offer several compelling advantages compared to the integrating imager. Typically, the efficiency in the energy range of 1-30 kVp is essentially 100%. More importantly, they are able to resolve the energy of the incoming X-rays, and it is possible to set internal thresholds such that only X-rays within a pre-selected range are detected and counted. By this approach, it is possible to essentially eliminate electronic and other sources of noise in the system. Furthermore, the contribution of the bremsstrahlung continuum part of the incoming X-ray spectrum is greatly reduced, thereby increasing the image contrast. One can think of each energy band in the detector as providing an image that is a monochromatic.

In some embodiments, a time stamped image may be obtained. An interesting result from time stamping, is the potential ability to correct for vibration without compensating mechanical motion. For example, if images are obtained at higher rates than typical mechanical vibration, then successive frames may be dynamically combined to reduce mechanical vibration effects.

Photon-counting detectors are now commercially available as complete assemblies with all electronics and computer interfaces optimized. Some of these systems are optimized to read on a pixel by pixel basis (e.g., Advacam AdvaPIX PTX3™ or Mars Spectral CT) while others read on a frame by frame basis (e.g., Dectris™) The Advacam PTX3 counts each individual event and, using pixel interpolation, can obtain a spatial resolution of 15 micron with 55-micron pixels. Count rates of up to 40 Mpixel/s are possible. The DECTRIS EIGER X 1M camera has 75-micron pixels and can have two energy thresholds set for the individual pixels. For a 1 Mpixel camera, up to 3000 frames per second are possible.

X-ray Optics for 3D Sample Tomography

FIG. 6A shows operating principles of an imaging device designed in accordance with some embodiments. As shown, a sample specimen 610 is mounted on a 3-axis stage, and the stage is programmed to execute a trajectory X(t), Y(t), Z(t) as function of acquisition time t. Sample specimen 610 may be an IC die, a biological specimen, or any other suitable target volume for imaging. As the stage moves, sources in the array are turned on and off selectively, while the detector (e.g., X-ray camera) obtains intensity images.

FIG. 6B shows a plan view diagram of an exemplary imaging system 600, in accordance with some embodiments. In FIG. 6B, imaging system 600 includes the source 602, specimen nano-positioner 604 and the detector 606, which are placed on a vibration isolated optical table 608.

In the example shown in FIG. 6A, the sources (filled circles) are turned on, whereas all other sources (unfilled circles) are turned off. At the next time step, a different set of sources are turned on to acquire an image at point X(t+1), Y(t+1), Z(t+1) on the trajectory. To avoid motion blur, the stage motion may be programmed to be “step-wise.” For example the stage may have sequential target positions and, every time it reaches a target the stage dwells (stops) until the image is acquired, prior to moving to the next target position. The images may be denoted as I_(XYZ) (x′,y′), where (x′,y′) denotes the coordinates on the detector.

The set of intensities I_(XYZ) (x′,y′) constitute the captured data used to reconstruct absorption and phase images of the sample specimen in accordance with some embodiments. The absorption and phase images may be denoted as a(x,y,z) and ϕ(x,y,z), respectively. Here, (x,y,z) denote the coordinates on the sample specimen, and can be thought as “voxels” of size 10 nm×10 nm×10 nm, or some other suitable voxel size. Physically, a(x,y,z) represents loss of X-ray photons due to absorption by atomic nuclei; therefore, at each voxel, a(x,y,z) may be determined primarily by the Z number of the elements within that voxel. On the other hand, ϕ(x,y,z) is determined by the dipole moment interactions between X-ray photons and the electron clouds of the elements within the voxel. Phenomenologically, this interaction is commonly referred to as “index of refraction,” and results in phase delay of the electromagnetic wave as it passes through matter. The interaction may be expressed simply as the complex transmittance ψ(x,y,z)=a (x,y,z)e^(iϕ(x,y,z)). It turns out that the two interactions, absorption and phase delay, are related through the Kramers-Kronig relationship that guarantees causality. Therefore, a(x,y,z) may be written as φ(x,y,z)γ(λ), where γ(λ) is a material- and wavelength-dependent coefficient. This relationship constitutes a strong prior, which is exploited in some embodiments to improve phase contrast from weakly absorbing objects.

The nature of the interactions a and φ determine the “forward operator,” which may be denoted as H. To construct H, two approximations are commonly used: the Born approximation, which assumes a weak phase object; and the Rytov approximation, which assumes weak gradients in the object's phase. The Rytov approximation is commonly used in the X-ray regime, as typically the weak gradient assumption is satisfied, whereas the phase delays may actually be significant. Some embodiments test this assumption against rigorous models as well as lab-bench experiments with known (calibrated) targets; and incorporate corrections into the construction of the forward operator. Assuming that the Rytov model holds, the phase of the field after propagating through the object may be expressed as:

$\begin{matrix} {{{\chi\left( {\overset{->}{r}}^{''} \right)} = {\frac{1}{g_{inc}\left( {\overset{->}{r}}^{''} \right)}{\int{\int{\int_{\underset{volume}{object}}{{g_{inc}\left( \overset{->}{r} \right)}\left( {1 - {\phi^{2}\left( \overset{->}{r} \right)}} \right){G\left( {{\overset{->}{r}}^{''} - \overset{->}{r}} \right)}d^{\; 3}\overset{->}{r}}}}}}},} & (1) \end{matrix}$

where g_(inc)({right arrow over (r)}) is the incident field; G({right arrow over (r)}) is Green's function of free space; and {right arrow over (r)}=(x,y,z), {right arrow over (r)}″=(x″, y″) are the coordinates within the object volume and on plane immediately beneath the object, respectively. The operator H is finally constructed by propagating the resulting attenuated and phase-delayed field from the plane {right arrow over (r)}″=(x″, y″) to the detector (e.g., camera) plane {right arrow over (r)}′=(x′, y′). The build-up of the forward operator H is complemented in some embodiments with experimental measurements, especially of the depth point spread function (PSF) as a spatially variant function under a certain limited scanning geometry, to improve the condition of the inverse problem.

Image Acquisition and Tomosynthetic Reconstruction

In some embodiments, the sample specimen is mounted on a high-precision nano-positioning stage, an example of which is discussed above in connection with FIG. 1. The mounted sample specimen may be sequentially exposed to X-ray illuminations in a coded fashion as discussed above. The X-ray optical chain described above in connection with FIG. 6A focuses the X-ray beam on a high-resolution detector in order to provide a set of projection images of the sample specimen such as an IC chip. In some embodiments, these projection images are reconstructed into a 3D image stack of planar images using a tomosynthesis algorithm.

Some embodiments combine coded source (structured) illumination, with strong priors applicable to ICs to define a tomosynthesis algorithm as an inverse problem, as follows:

Given the set of measurements I_(XYZ)(x′,y′), find the complex transmittance ψ(x,y,z) that minimizes the Error Functional ∥I _(XYZ)(x′,y′)−Hψ(x,y,z)∥² +μR(ψ),  (2)

where R(ψ) is a regularizer expressing prior knowledge about the complex transmission function, as discussed in more detail below, and μ is the regularization parameter. Alternatively, the problem may be posed as one of constrained optimization: Minimize R(ψ) subject to ∥I _(XYZ)(x′,y′)−Hψ(x,y,z)∥²<ε,  (3)

where ε is a “robustness” parameter expressing confidence in the final estimate deviating from the data in order to avoid over fitting.

A broad range of optimization techniques are available for tackling this class of problems, e.g. TwIST for (2), Matching Pursuit and its variants (e.g., Orthogonal Matching Pursuit) for (3), and others. Different techniques can be compared in terms of performance, including accuracy of reconstruction with simulated and calibration samples; and in terms of convergence speed.

The choice of regularizer R(ψ) may be important. If w belongs to a class of objects that can be expressed sparsely, i.e., a set of basis functions exists such that the projection of w to this set yields very few non-zero coefficients, then equations (2) and (3) can be proven to converge to the correct solution with probability near 1 even if the measurements are severely undersampled. A limit indeed exists as to how many samples are required at the minimum to obtain the high quality reconstruction; below this limit, the reconstruction typically fails. However, most classes of objects of interest are indeed “sparsifiable,” i.e., a set of basis functions does indeed exist in which the objects in the class are sparse.

Sparsity criteria can be very general. In the case of IC dies that are of interest for imaging with an imaging system designed in accordance with some embodiments, priors that can be translated into sparsity are: the Manhattan geometry of the ICs; the layering into equally spaced, parallel layers; and the presence of a limited number of elements whose indices of refraction in the X-ray regime has been tabulated for various stoichiometries and obey the phase-attenuation duality relationship. Moreover, the random multiplexing of several sources at each measurement, as shown in FIG. 6A, meets the “incoherence” criterion of compressive sensing. Optimization schemes generally allow for the inclusion of other constraints, e.g. positivity of the absorption coefficient a(x,y,z),

In some embodiments, an ad hoc approach is applied to determine the regularizer R(ψ) according to the criteria described above. This technique has yielded good results in prior work. To further enhance resolution, some embodiments adopt a “learning-enhanced inversion” approach. Some embodiments train a compressed representation of imaged ICs (e.g., an overcomplete dictionary or set of neural network hidden units) using high-resolution images of sample/representative ICs. The dictionary may be used to denoise and enhance the resolution of the computationally reconstructed images. Dictionary learning is particularly suitable for some embodiments since the number of elements in an IC should be limited and the patterns of these elements should be easy to learn.

A tomosynthesis algorithm in accordance with some embodiments is capable of classifying each voxel (e.g., according to material composition, part of a standard circuit component such as a logic gate, etc.) for identifying areas of interest. These computational imaging and learning-enhanced approaches yield a “compressive” gain, defined as: G=number of voxels (unknowns) in the object/number of measurements.

As a rule of thumb, the number of measurements required to obtain a 3D reconstructed image approximately equals the number of voxels desired to reconstruct, i.e., G≈1; in turn, that is determined by the desired resolution. For an IC of lateral size 1 mm×1 mm and thickness 100 μm, and desired resolution of 10 nm×10 nm×10 nm, 50,000×50,000×5000 voxels are required. Collecting so many measurements is typically impractical; instead, by using sparse representations and the inversion formalisms using equations (2) or (3) in accordance with some embodiments, the same amount of information may be obtained but with a much smaller number of measurements, i.e. G>>1. Because there are limited number of elements in the IC images, the image representation are generally sparse, and the compress gains in the Table 1 below may be achieved in some embodiments.

FIG. 9 is a schematic flow diagram showing an exemplary process 900 for computational imaging and reconstruction of a biological specimen, in accordance with some embodiments. In such embodiments, The goal of computational imaging activity is the reconstruction of the cellular structure of the specimen with the desired resolution of 10×10×10 nm-cube, using the intensity images obtained from the optical chain.

FIG. 9 illustrate several components of process 900, which are discussed in detail below.

Design of illumination: To capitalize on the new flexibility offered by the distributed source, we will design illumination patterns, to be used in sequence as the specimen is being mechanically scanned and rotated. The patterns will be optimized to work with the remaining computational imaging components.

Forward problem: This is a rigorous simulation-based model of X-ray propagation through a specimen, assuming an internal structure is given. Of course, the purpose of this program is to image unknown specimen; however, the step of developing the “forward operator” is essential for the next steps, as we detail below.

Inverse Problem: Utilizing knowledge of the forward operator, and with the designed illumination patterns from the previous two components, this part of the computational engine converts a sequence of “raw images” (i.e., intensity patterns detected at the camera) into the estimate of the tomographic structure of the specimen. This is accomplished in two sub-steps:

Inverse problem part 1, field retrieval: The first step is to convert the raw intensity images into estimates of the complex field (amplitude and phase; or real and imaginary part) that emerged from the specimen in any given measurement. At first, it might appear that there is not enough information to go from a real positive measurement to a complex estimate; to overcome that limitation, we will use advanced machine learning algorithms that exploit the specimen structure and material composition.

Inverse problem part 2, tomography: The second step is to collect the complex field estimates (from part 1) at multiple specimen positions and rotations and use them to estimate the specimen's internal structure. We will carry it out using advanced compressive sensing algorithms as well as machine learning.

Reconstruction Sub-system: a tomosynthesis reconstruction algorithm for layer-by-layer structure of a sample using an iterative reconstruction method and advanced post image processing techniques may be used. In one implementation, an iterative image reconstruction is used.

Experimental Calculations for Performance Estimates

To determine the acquisition time required, the expected exposure time per image and total number of images required for reconstruction were computed. The results are summarized in Table 1.

Reconstruction Detector Compress Exposure Acquisition Resolution volume Resolution gain time time 20 nm 50,000 × 50,000 × 2500 3000 × 3000 20 38.4 s 15.43 days 1.92 s 18.5 hours 10 nm 100,000 × 100,000 × 5000 3000 × 3000 >20 <1.92 s <6.2 days 10 nm 100,000 × 100,000 × 5000 3000 × 3000 >20 <1.92 s <6.2 days

The photon flux at each detector pixel as function of number of photons generated by each nano-focused source is P _(detector) =P _(source) Mηe ^(Nμd),

where P_(detector) is the intensity at the detector, P_(source) is the intensity of the source, M is the number of sources simultaneously illuminating the detector, η is the fraction of photons emitted per source that are captured by the detector (for a 1 μm pixel size and a 1 cm source-to-detector distance, η=7.958×10⁻¹⁰), and e^(Nμd) is the transmission coefficient of the IC. For a 5 nm focal spot size, an imaging system in accordance with some embodiments is expected to deliver 10 mW of power per anode. The estimated photon production efficiency of the system is 1%. The expected photon intensity that the source emits in all directions (P_(source)) is 6.242×10⁶ photons/s.

To estimate the attenuation introduced by the IC, a representative IC for which the silicon substrate has been thinned to 50 μm, and consisting of: a 50 μm thick silicon substrate, 26 layers of 100 nm thick silicon nitride, and 13 layers of 100 nm thick copper vias was considered. The transmission coefficient for this IC at 10 keV X-ray energies is 0.5243.

On average it is expected to capture 1000 X-ray photons per pixel to generate a useable image with noise variance. For M=1, or single source imaging, this results in an exposure time of 38.4 s. To further increase the photon flux and decrease the required exposure time, coded illumination patterns with M˜20 may be used, resulting in an exposure time of 1.92 s.

To calculate overall acquisition time, the number of images necessary to reconstruct the IC region of interest was considered. High-resolution X-ray detectors typically provide 9 megapixels of resolution. An IC having a region of interest of 1 mm×1 mm×0.05 mm was assumed. Aiming at a “compressive gain” of 20× and using the multiple images acquired on the spiral-like trajectory of FIG. 6A to obtain the rest of the necessary data, the expected acquisition times of Table 1 were calculated. In some embodiments, exposure time may be reduced by improvements in source flux, detector efficiency, and/or optimization of the imaging geometry. Compressive gain may also improve as machine learning algorithms are incorporated.

To achieve the lateral resolution required for IC imaging, some embodiments combine geometric magnification with computational imaging. Starting with a detector pixel pitch of 1 μm, geometric magnification is used in some embodiments, as shown in FIGS. 1A and 6. In some implementations, a magnification factor of 12.5-100 may be used, resulting in an effective pixel size of 10-80 nm. To increase the resolution of the system, computational imaging using structured illumination from the X-ray source may be used with one or more of the inversion tomosynthetic algorithms discussed above. Coded illumination and synthetic aperture imaging have been shown to improve effective resolution by a factor of 4 in each direction, and by exploiting the additional strong priors of Manhattan geometry, phase-attenuation duality, and material composition, further gains in resolution are achievable. Using conservative estimates, a system designed in accordance with some embodiments will achieve a lateral resolution of 2.5-20 nm.

For computational imaging, unlike direct imaging techniques such as confocal microscopy, the vertical resolution is not as clearly defined as the lateral resolution. It is useful to consider the depth of field of the system as a loose lower bound to the vertical resolution. For computational imaging methods such as coded illumination and tomosynthesis, the effective numerical aperture of the system is much larger than the numerical aperture of the optical system because many views of the object are captured from many angles or patterns of illumination. In tomosynthesis reconstruction, this effect manifests as a blurring between reconstruction planes. Compressive tomosynthesis methods have been shown to greatly improve vertical resolution, allowing for reconstruction slices at a resolution comparable to the lateral resolution. The techniques described herein combine features from ptychography and compressive tomography in conjunction with strong priors, and, optionally, learning-enhanced inversion methods; hence, the vertical resolution of a system designed in accordance with some embodiments will achieve a vertical resolution approximately equal the lateral resolution (e.g., 2.5-20 nm).

One aspect of the present application is directed to a novel X-ray nanoscope (XN) imaging technology in 2D projection domain as well as using limited angle tomography (i.e., Tomosynthesis) and 3D computed tomography for imaging biological specimen. Examples are provided herein for testing biological and preclinical applications using such imaging systems. In some embodiments, a labeling agent comprising a heavy or high atomic number element is provided to label the biological specimen prior to imaging on the stage to enhance molecular contrast in biological samples.

First Example of Biological Testing: Imaging Cellular Uptake of Metallo-Nanoparticles Detectable by the X-Ray Nanoscope

The inventors have recognized and appreciated that a number of nanoparticle formulations have been developed and several of them have progressed to clinical trials. Well over a thousand patients have now received chemically distinct, nano-encapsulated chemotherapeutics, generally showing lower toxicity, increased tumoral accumulation of payloads and occasionally improved progression-free survival. However, cures have been rare and most of these cancers will ultimately become resistant, therefore highlighting the need for new strategies and a basic understanding on how these materials work in cells. For example, very little is known about i) how and in which cells these nanomaterials accumulate in to elicit their effect, ii) what the rules are to improve efficacy so that new preparations can be designed rationally rather than empirically, and whether one could predict who will respond to a given therapeutic nanoparticle (TNP) in the clinic. The goal of this example is to perform analyses of a model X-ray dense nanoparticle at the single cell level to address key questions on distribution and accumulation.

In one embodiment, ferumoxytol, an iron containing dextran coated nanoparticle (NP) is chosen as a labeling agent for X-ray imaging, although it should be appreciated that other labeling agent known in the art to be suitable for labeling biological specimen with a heavy element can also be used and aspects of the present application is not limited to ferumoxytol. The choice of ferumoxytol was in part driven by several considerations. For example, FDA has approved this product as a drug (Feraheme). The iron core within ferumoxytol will be directly detectable by XN and the nanoparticle primarily targets macrophages. The inventors have also appreciated and recognized that fluorescent versions of ferumoxytol may additionally be used to provide a correlation study between X-ray image obtained from an X-ray nanoscope to flow cytometric data and microscopy. Ferumoxytol as a labeling agent has been demonstrated to have sustained and rapid (within minutes) uptake into macrophages and can thus be used for clinical macrophage assessment by magnetic resonance imaging (MRI) or generically to image inflammation. A major clinical obstacle however, has been the difficulty to interpret MR images as the cellular distribution and compartmentalization of Feraheme was largely unknown and had relied on occasional histopathology correlation (iron stains are not very sensitive). The introduction of fluorescent Feraheme nanoparticles and high resolution intravital microscopy has shed some light on the behavior of these imaging nanoparticles, but more work remains to be done through the use of an X-ray nanoscope (XN) as outlined below.

One aspect is directed to in vivo labeling of innate immune cells in inflammation. In an example, to test if NPs label innate immune cells, murine bone marrow derived macrophages and brain microglia are first incubated with an NP called CLIO (cross-linked iron oxide nanoparticles) conjugated with the fluorescent probe FITC. The labeled cells are imaged with confocal microscopy and assessed CLIO uptake in the cells by flow cytometry.

Neuroinflammation may be induced using encephalitogenic proteins in mouse brains and injected CLIO intravenously. It is found that CLIO preferentially labeled innate immune cells (macrophages and microglia) but not adaptive immune cells in the inflamed brain.

Another aspect is directed to in vitro cellular XN imaging to investigate ferumoxytol uptake in macrophages. In an example, to determine the compartmentalization and effect of iron cores in single macrophages on XN imaging, bone marrow derived macrophages or RAW 264.7 cells may be pulse incubated with ferumoxytol-VT680 at 0, 100 μg/mL, and 500 μg/mL in DMEM with 10% fetal bovine serum (FBS). Samples will be collected at the following time points to determine the time course of ferumoxytol processing in living macrophages: pre-incubation, 10 min, 30 min, 1 hour, 4 hours, 8 hours, and 24 hours after incubation. The cell suspensions will be washed with media and then re-suspended to remove extracellular ferumoxytol at each time point. The final suspension will be imaged on the X-ray nanoscope to obtain cellular images. After imaging, the samples will undergo flow cytometry to quantify the degree of ferumoxytol uptake in the macrophages. The flow cytometric data will be correlated with the cellular imaging data.

Yet another aspect is directed to 3D cellular tomographic imaging of macrophage trafficking in a mouse model of wound healing. In some embodiments, XN phase imaging information may be used in the clinical environment. For example, ferumoxytol in vivo in a wound healing mouse model may be used to track the trafficking of macrophages into the wound over time. The mice will be imaged on days 1, 4 and 7 after injury with MR imaging. 20 mg of Fe/kg of ferumoxytol-VT680 will be administered intravenously via the tail vein. The mice will be imaged at the time point of highest uptake guided by the in vitro testing above. MR imaging will be performed on a 7T small animal MRI scanner (Bruker, Billerica, Mass.) with a dedicated mouse body coil. The size of the wound will be measured by photomicrograph taken after imaging.

X-ray cellular imaging may be performed in addition to MR imaging, such that the same biological specimen such as tissues may be analyzed by more than one imaging technique and the results correlated to produce more accurate diagnostic results than a single imaging technique alone. For example, after MR imaging, the mice will be euthanized, excised wounds will be bisected in the caudocranial direction and embedded in O.C.T. compound (Sakura Finetek) and snap-frozen in a 2-methylbutane bath on dry ice. Tissue blocks (20 μm) of the wound will be prepared and imaged on the X-ray nanoscope.

Flow cytometry may also be performed and to correlate with imaging results. For example, following XN imaging, the tissue block may be processed into single cell suspensions for flow cytometry to quantitatively determine the degree of macrophage uptake of ferumoxytol. Through these procedures, we will be able to directly visualize how ferumoxytol cellular uptake in macrophages changes as wound injury evolves and heals, and correlate to how such change affects the in vivo MRI visualization of nanoparticles and macrophage trafficking.

Second Example of Biological Testing: Imaging Lymphoma Samples and Rituximab-CD20 Interaction

Rituximab is a well-characterized, chimeric monoclonal antibody that targets the protein CD20, which is widely expressed on B cells at most stages of development. The binding of Rituximab to B cells results in apoptosis of both malignant and normal B cells that express CD20. For this reason, Rituximab is utilized as a biologic to treat B-cell lymphoma, and other B-cell mediated disorders, such as rheumatoid arthritis, idiopathic thrombocytopenic purpura and myasthenia gravis.

The inventors have recognized and appreciated that it is possible to modify antibodies in general, and rituximab in particular, with gadolinium or other heavy element, such that the modified antibodies act as labeling agents. Fluorescent and paramagnetic/X-ray dense chelator versions of rituximab (rituximab-VT680; rituximab-DOTA-Gd) have been developed as reported in Pathania et. al., Holographic assessment of lymphoma tissue (halt) for global oncology field applications. Theranostics. 2016; 6:1603-1610; and Turetsky et. al., On chip analysis of cns lymphoma in cerebrospinal fluid. Theranostics. 2015; 5:796-804. These versions of rituximab will allow us to image in real time using an X-ray nanoscope i) the cellular association and ii) resultant cellular changes at high spatial and temporal resolutions. The X-ray signatures of labeled vs unlabeled rituximab may also studied and compared through phase contrast imaging.

According to an aspect, two strategies may be employed to evaluate the ability of an XN to directly visualize the interaction between a therapeutic agent (rituximab) and its target on B cells (CD20).

The first strategy is in-vitro assessment of cellular tomography via the use of XN. Interactions between rituximab-Gd, rituximab and CD20 expressed by a monomorphic population of B cells may be directly visualized in-vitro. To this end, Daudi cells (ATCC CCL-213), a Burkitt cell line with robust CD20 expression, will be cultured in RPMI with 10% FBS and 1% pen/strep antibiotic. Rituximab analogs will be added to each culture at concentrations of 0, 30 nM or 60 nM. Similarly, K562 cells (ATCC CCL-243) a CD20 negative leukemic cell line, will be utilized as a CD20 negative control cell line. Six aliquots of each culture will be obtained at several time points (1 hour prior to rituximab; and 1 hour, 4 hour and 8 hours after administration). Aliquots will be either placed in a cuvette for evaluation of rituximab binding using XN imaging or incubated with a FITC-conjugated secondary antibody and assessed by flow cytometry for rituximab binding. Rituximab binding will be qualitatively and quantitatively assessed by review of changes in the cellular morphology observed by cellular imaging using XN at each concentration of rituximab and time-point assessed. Data obtained from XN images with regards to rituximab binding will then be directly compared with binding data (e.g., percent of cells bound to Rituximab) obtained by flow cytometric analysis.

In a second strategy, rituximab binding after in-vivo administration will be studied, to assess the ability of cellular imaging using XN to accurately quantify drug binding after its in-vivo administration and in the context of a heterogeneous population of cells. Patient-derived xenografts models of Daudi cell lines may be used, as well as primary human lymphomas (e.g., diffuse large B-cell lymphoma). Established patient derived xenograft models generated by tail vein injection of Daudi cell line or primary lymphoma cells (diffuse large B cell lymphoma) into SCID mice will subsequently be treated with Rituximab by tail vein injection. Peripheral blood will be obtained from each animal 5 days prior and 24 hours after initiation of Rituximab therapy. Peripheral blood samples will be assessed for Rituximab binding by cellular tomography as well as by flow cytometric analysis after incubation with FITC-conjugated secondary antibody targeting Rituximab. Quantification of Rituximab binding based on XN imaging will be directly compared analogous quantification of binding by flow cytometry. With this approach, we will be able to directly evaluate the ability of cellular imaging to assess drug-target interactions occurring after in-vivo drug administrations. In addition, we will be able to assess the XN technology's ability to analyze cellular morphologic changes related to drug binding in the context of a heterogeneous population of peripheral blood cells of various sizes and levels of target expression.

In addition to assessment of drug-target interactions, another aspect is directed to medical applications of XN imaging for assessment of pathological specimen. High resolution volumetric XN imaging of core biopsy samples of lymphoma specimen obtained from the above mouse models and/or surgical specimen may be performed. These imaging results will allow for a “preprocessing” of entire samples prior to cutting and directing attention to areas of interest. Assessing an entire tissue specimen using XN may facilitate study of tissue heterogeneity and phase contrast in surgical samples.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware or with one or more processors programmed using microcode or software to perform the functions recited above.

In this respect, it should be appreciated that one implementation of the embodiments of the present invention comprises at least one non-transitory computer-readable storage medium (e.g., a computer memory, a portable memory, a compact disk, etc.) encoded with a computer program (i.e., a plurality of instructions), which, when executed on a processor, performs the above-discussed functions of the embodiments of the present invention. The computer-readable storage medium can be transportable such that the program stored thereon can be loaded onto any computer resource to implement the aspects of the present invention discussed herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs the above-discussed functions, is not limited to an application program running on a host computer. Rather, the term computer program is used herein in a generic sense to reference any type of computer code (e.g., software or microcode) that can be employed to program a processor to implement the above-discussed aspects of the present invention.

Various aspects of the present invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and are therefore not limited in their application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

Also, embodiments of the invention may be implemented as one or more methods, of which an example has been provided. The acts performed as part of the method(s) may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Such terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term).

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing”, “involving”, and variations thereof, is meant to encompass the items listed thereafter and additional items.

Having described several embodiments of the invention in detail, various modifications and improvements will readily occur to those skilled in the art. Such modifications and improvements are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description is by way of example only, and is not intended as limiting. The invention is limited only as defined by the following claims and the equivalents thereto. 

What is claimed is:
 1. A method of imaging a biological specimen using an imaging system comprising an X-ray source having a plurality of electron sources, an X-ray detector and a stage arranged between the X-ray source and the X-ray detector and configured to have mounted thereon the biological specimen, the method comprising: moving, using at least one controller, the stage during operation of the imaging system; and selectively activating at least a subset of the electron sources during movement of the stage to acquire a set of intensity data by the X-ray detector as the stage moves along a three-dimensional trajectory.
 2. The method of claim 1, further comprising: labeling the biological specimen with one or more labeling agent comprising a heavy element; and providing the labeled biological specimen on the stage.
 3. The method of claim 2, further comprising: with at least one processing computer, receiving the set of intensity data output from the X-ray detector captured along the three-dimensional trajectory; performing a tomosynthetic reconstruction process using the set of intensity data to reconstruct a three-dimensional image of the biological specimen.
 4. The method of claim 3, wherein performing a tomosynthetic reconstruction process comprises reconstructing absorption and phase images of the biological specimen.
 5. The method of claim 4, further comprising: with the at least one processing computer, analyzing spatial distribution of the one or more labeling agent within a single cell of the biologic specimen based on the absorption and phase images.
 6. The method of claim 5, further comprising: capturing one or more light images representing at least a portion of the biological specimen using a light microscope; and with the at least one processing computer, correlating the absorption and phase images of the biological specimen with the one or more light images.
 7. The method of claim 4, wherein performing a tomosynthetic reconstruction process comprises solving an inverse problem using a sparse representation of an image representation.
 8. The method of claim 2, wherein the heavy element has an atomic number of at least
 20. 9. The method of claim 8, wherein the one or more labeling agent comprises iron, iodine, gadolinium, or combinations thereof.
 10. The method of claim 2, wherein the one or more labeling agent comprises nanoparticles that comprise iron.
 11. The method of claim 2, wherein the one or more labeling agent comprises an antibody labeled with a heavy element having an atomic number of at least
 20. 12. The method of claim 1, wherein the biological specimen comprises a single cell.
 13. The method of claim 1, wherein the biological specimen comprises a liquid.
 14. The method of claim 13, wherein the biological specimen is supported in a matrix.
 15. The method of claim 1, further comprising: mounting the biological specimen on the stage via a glass slide.
 16. The method of claim 1, wherein moving the stage comprises: moving the stage with a 1 nm translation resolution and 7 mm travel in an x-y plane parallel to the X-ray detector and a 0.2 nm resolution and 100 μm travel in a z-direction perpendicular to the x-y plane.
 17. The method of claim 1, wherein the three-dimensional trajectory is a three-dimensional spiral.
 18. An imaging system for imaging a biological specimen, the imaging system comprising: an X-ray source including a plurality of spatially and/or temporally addressable electron sources; an X-ray detector arranged such that incident X-rays are oriented substantially normal to an incident surface of the X-ray detector; a stage arranged between the X-ray source and the X-ray detector, the stage configured to have mounted thereon the biological specimen through which X-rays generated by the X-ray source pass during operation of the imaging system; and at least one controller configured to: move the stage during operation of the imaging system; and selectively activate at least a subset of the electron sources during movement of the stage to acquire a set of intensity data by the X-ray detector as the stage moves along a three-dimensional trajectory.
 19. The imaging system of claim 18, wherein the plurality of spatially and temporally addressable electron sources comprise a silicon field emitter array of cold cathodes.
 20. The imaging system of claim 19, wherein each of the cold cathodes in the silicon field emitter array includes a silicon nanowire current limiter.
 21. The imaging system of claim 20, wherein each of the cold cathodes in the silicon field emitter array comprises a narrowing tip.
 22. The imaging system of claim 18, wherein the X-ray source further comprises an anode structure arranged to transmit electrons generated by the plurality of electron sources toward the X-ray detector.
 23. The imaging system of claim 22, wherein the anode structure comprises a thin membrane window.
 24. The imaging system of claim 23, wherein the X-ray source comprises an ultra-high vacuum enclosure arranged between the plurality of electron sources and the thin membrane window.
 25. The imaging system of claim 24, wherein the thin membrane window comprises one or more graphene layers.
 26. The imaging system of claim 24, wherein the thin membrane window is impermeable to gas or liquid.
 27. The imaging system of claim 14, wherein the at least one controller is further configured to move the stage with a 1 nm translation resolution and 7 mm travel in an x-y plane parallel to the X-ray detector and a 0.2 nm resolution and 100 μm travel in a z-direction perpendicular to the x-y plane.
 28. A method of imaging a biological specimen using an imaging device comprising an X-ray source having a plurality of electron sources, an X-ray detector and a three-axis stage arranged between the X-ray source and the X-ray detector and configured to have mounted thereon the biological specimen, the method comprising: moving, using at least one controller, the three-axis stage along a three-dimensional trajectory during operation of the imaging device; activating a first subset of the electron sources to acquire a first set of intensity data by the X-ray detector when the three-axis stage is at a first position along the three-dimensional trajectory; activating a second subset of the electron sources different from the first subset to acquire a second set of intensity data by the x-ray detector when the three-axis stage is at a second position different from the first position along the three-dimensional trajectory; and performing a tomosynthesis reconstruction process using the first and second sets of intensity data. 